Retail

Modern businesses place their customers’ experience as a high priority and are proactive in crafting the perfect Customer Experience Management (CEM or CXM) strategy. Effective CEM involves knowing a customer thoroughly, addressing their dynamic needs, fulfilling their expectations, ensuring that they are satisfied at every point of interaction with a company, and creating or upholding a company’s positive perception. Companies realize the importance of existing customer journeys, understand the touchpoints and experiences that customers have with a brand — from discovery, presales, sales, customer service, and beyond.  AI-powered white-space analysis for sales can recommend immediate action on opportunities, assist companies in crafting personalized messages at scale, and improve CEM by reducing the time to respond. Automated systems can distribute real-time offers based on predicted behaviors, manage and update databases without prompting, and execute an intelligent multi-channel campaign. By automating the decision-making process, they create consistent, expected interactions that lead to much-needed unified customer experiences (CX).

While strategizing, decision-makers must efficiently figure out whether to build or invest in procuring an AI-driven CEM solution. They also need to find out if enough skills and budget are present to create it in-house or buying an AI MLOps solution is a better fit. Companies need the ability to consolidate and analyze data across multiple touchpoints and channels, capture customers’ activity online, in-store, on a social network, or at any other point where they interact with a brand. To understand the efficiency and success of a company’s CEM, different metrics that add up to a company’s CEM should also be readily measurable and available.

By combining the power of our flagship framework — xpresso.ai — with a decade of experience, we have ensured AI projects are delivered in a timely and robust manner, and organizations achieve a successful AI transformation journey with our systematic approach towards enterprise AI and MLOps solutions.  

Our approach to an enterprise MLOps journey begins with a robust data intake and elaborate analysis to frame the problem areas.  The second part includes applying an engineering mindset, identify enablers required for AI initiatives to be successful at enterprise scale, and preparing cognitive models. Finally, these models are deployed and managed throughout the lifecycle of the solution. Throughout the lifecycle, xpresso.ai pre-built frameworks are used to push the required transformations ahead. Coupled with a robust uptime for applications we use and near-zero chances of interruption, this means unwavering support for customers. So, what would have typically taken months to deliver can be developed and deployed in weeks, and most importantly — at a fraction of the cost.

Challenges

Data is inadequate, despite the volume: Although companies have a wealth of valuable data via their CX tools, customer service, sales representatives, social media, and CEM metrics, most of these fails to reveal rich real-time customer insight. 

Inconsistent experiences can thwart customer experience: Customers today have high expectations from their brand interactions — they want to be recognized while browsing offline or online stores and want their loyalty plans to be identified and updated quickly during purchase. Faithful and long-term customers expect those brands to remember their preferences and anticipate their needs when they return. 

There is no unifying strategy for managing multiple touchpoints: While interacting with a brand, a customer only sees the brand itself. This is a vital realization while devising a unified CX strategy because this involves discarding internal siloes, tunnel-vision approaches sales and marketing, and zero in upon what matters most to their customer.

No universal marketing tech stack: Though a unified CX strategy is essential, the right tools to collect relevant data, ability to build a single customer view are also needed. A proactive approach to offering customers consistently positive experiences across touchpoints and channels is essential as there is no perfect implementation strategy for every enterprise. 

Lack of adequate technological abilities: Without real-time data parsing and learning about the customer dynamically, creating a single customer view (SCV) — the first place to start — can be impossible. To execute a truly unified CX strategy, brands must first have the ability to consolidate and analyze data across multiple touchpoints and in an omnichannel environment. Lack of an adequate omnichannel listening platform: For a fast-growing company, such a platform may not be capable of efficiently handling connectivity to analytics and can’t gather actionable knowledge.

Solution Approach

Use Case Discovery

We actively engaged with our client to capture the business requirements while observing the problem. We identified the relevant datasets and formulated a use case-based approach that would solve the business problem or improve/predict actionable insights to mitigate the problem. 

We assisted in setting up the required infrastructure – the xpressso.ai framework provides out-of-the-box development platforms. All functionalities can be accessed using a Jupyter Notebook — ensuring zero-delay and plug-and-play availability of high-end hardware. Development images configured based on pre-defined templates can be installed on-premises or in a development VM within the xpresso.ai infrastructure. This enables authentication using LDAP, seamless project setup using Bitbucket, Jenkins, and Docker (ensuring build and deployment without software compatibility issues).  The project was started seamlessly with the relevant environments, which are subsequently created automatically.

The framework made available by xpresso.ai leverages the latest ML and DL tools while preparing models and includes Pachyderm-based data versioning, deployment using a Kubernetes orchestration system, Kubeflow and Spark-based ML and DL build and deployment, Istio-based service mesh enabled microservice architecture, and ELK based monitoring capability; contributing to reduction in latency time.

Data Engineering

xpresso.ai’s MLOps framework has different data adapters available through a common catalog of services that simplify interoperability and scalability concerns, enable APIs, and abstract all the technical complexities from the service consumer. This allows establishing high-end Alluxio and Presto-based rapid, inexpensive data connectivity and data collection from diverse sources (available in structured, unstructured, and streaming formats) coming in at a high velocity and in huge volumes. 

All the data sources are funneled into the data storage layer after proper validation and cleansing. The storage landscape with different storage types and extreme flexibility is built-in to manipulate, filter, select, and co-relate different data formats.

Infrastructure and MLOps Automation

The details collected, project code, data preparation workflows, and models can be easily versioned in a repository (Bitbucket, Git, etc.), and data sets can be versioned through on-premises/cloud storage. These can be added as exploratory variables by using two excellent features of xpresso.ai: 

1. Data Connectivity Marketplace libraries
2. Data Versioning

The attributes obtained are used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate, and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis).  Different datasets and their different versions can be easily controlled and stored into xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. 

xpresso.ai MLOps automation allows creating pipelines, train with as much data and as accurately as possible, fastest time to inference, with the ability to rapidly retrain. The xpresso Data Pipeline Management (Rapid Model Training and Experimentation) uses Kubeflow-enabled pipelines. Thus, multiple experiments using different models and datasets can be created, tested, paused, and restarted to gain better insight. 

We assisted our client’s brand custodian team in significantly improving their operational efficiency and working towards a unified CEM strategy by providing them with almost real-time actionable insights.